Import Dataset
#Import data
week_kuds <- read.csv("week_kuds2.2.csv", sep=";") #has the wrong week values
week_kuds3 <- read.csv("week_kuds1 - usar.csv", sep=";") #has the right week values
week_kuds$Week <- week_kuds3$Week #substitute the wrong for the right week in the dataset
#create week variable without the year
names(week_kuds)[names(week_kuds) == "Week"] <- "WeekYear"
week_kuds$Week <- substr(week_kuds$WeekYear, 1, 2)
#create year variable without the week
week_kuds$Year <- substr(week_kuds$WeekYear, 4, 7)
week_kuds$File <- as.factor(week_kuds$File)
week_kuds$Species <- as.factor(week_kuds$Species)
week_kuds$Transmitter <- as.factor(week_kuds$Transmitter)
week_kuds$KUD50 <- as.numeric(week_kuds$KUD50)
week_kuds$KUD95 <- as.numeric(week_kuds$KUD95)
week_kuds$Habitat <- as.factor(week_kuds$Habitat)
week_kuds$Migration <- as.factor(week_kuds$Migration)
week_kuds$ComImport <- as.factor(week_kuds$ComImport)
week_kuds$Length_cm <- as.numeric(week_kuds$Length_cm)
week_kuds$LengthStd <- as.numeric(week_kuds$LengthStd)
week_kuds$BodyMass <- as.numeric(week_kuds$BodyMass)
week_kuds$BodyMassStd <- as.numeric(week_kuds$BodyMassStd)
week_kuds$Longevity <- as.numeric(week_kuds$Longevity)
week_kuds$Vulnerability <- as.numeric(week_kuds$Vulnerability)
week_kuds$Troph <- as.numeric(week_kuds$Troph)
week_kuds$ReceiverDensity <- as.numeric(week_kuds$ReceiverDensity)
week_kuds$MonitArea_km2 <- as.numeric(week_kuds$MonitArea_km2)
week_kuds$MCP_km2 <- as.numeric(week_kuds$MCP_km2)
week_kuds$NReceivers <- as.numeric(week_kuds$NReceivers)
week_kuds$MaxDistReceivers <- as.numeric(week_kuds$MaxDistReceivers)
week_kuds$MaxLength <- as.numeric(week_kuds$MaxLength)
week_kuds$MaxBodyMass <- as.numeric(week_kuds$MaxBodyMass)
week_kuds$a <- as.numeric(week_kuds$a)
week_kuds$b <- as.numeric(week_kuds$b)
week_kuds$Week <- as.factor(week_kuds$Week)
week_kuds$Year <- as.factor(week_kuds$Year)
week_kuds$Spawn <- as.factor(week_kuds$Spawn)
week_kuds$Spawn <- with(week_kuds, ifelse((SpawnSeason == "SS" & Week %in% c("11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40")) |
(SpawnSeason == "A" & Week %in% c("41", "42", "43", "44", "45", "46", "47", "48", "49", "50")) |
(SpawnSeason == "W" & Week %in% c("51", "52", "53", "54", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10")),
"yes", "no"))
week_kuds$SpawnSeason <- as.factor(week_kuds$SpawnSeason)
boxplot(KUD95 ~ Spawn, data= week_kuds, col="deepskyblue")
boxplot(KUD50 ~ Spawn, data= week_kuds, col="green2")
#Comparar as médias dos KUDs dos individuos que se encontravam em época reprodutiva ou não
#escolhemos o teste wilcox porque não assume normalidade nos dados e é útil para grandes e pequenas amostras
wilcox.test(week_kuds$KUD95~week_kuds$Spawn) #de acordo com o teste realizado parece não haver evidências para afirmar que a home range varia consoante a Epoca Reprodutiva, visto que o p-value toma o valor de 0.5623 que é maior do que o nivel de significância 0.05
##
## Wilcoxon rank sum test with continuity correction
##
## data: week_kuds$KUD95 by week_kuds$Spawn
## W = 81731513, p-value = 0.5623
## alternative hypothesis: true location shift is not equal to 0
wilcox.test(week_kuds$KUD50~week_kuds$Spawn) #de acordo com o teste realizado parece não haver evidências para afirmar que a core area varia consoante a Epoca Reprodutiva, visto que o p-value toma o valor de 0.9972 que é maior do que o nivel de significância 0.05
##
## Wilcoxon rank sum test with continuity correction
##
## data: week_kuds$KUD50 by week_kuds$Spawn
## W = 81393886, p-value = 0.9972
## alternative hypothesis: true location shift is not equal to 0
glmm_total_kud95 <- glmmTMB(KUD95 ~ Spawn + (1|File) + (1|Transmitter), data=week_kuds, family = Gamma(link="log"))
summary(glmm_total_kud95)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | File) + (1 | Transmitter)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 9685.9 9726.6 -4837.9 9675.9 25607
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## File (Intercept) 0.11301 0.3362
## Transmitter (Intercept) 0.07224 0.2688
## Number of obs: 25612, groups: File, 48; Transmitter, 850
##
## Dispersion estimate for Gamma family (sigma^2): 0.0747
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.077092 0.051262 1.504 0.133
## Spawnyes 0.055785 0.003804 14.663 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
glmm_total_kud50 <- glmmTMB(KUD50 ~ Spawn + (1|File) + (1|Transmitter), data=week_kuds, family = Gamma(link="log"))
summary(glmm_total_kud50)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | File) + (1 | Transmitter)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## -76507.0 -76466.2 38258.5 -76517.0 25607
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## File (Intercept) 0.08314 0.2883
## Transmitter (Intercept) 0.06028 0.2455
## Number of obs: 25612, groups: File, 48; Transmitter, 850
##
## Dispersion estimate for Gamma family (sigma^2): 0.0603
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.508216 0.044203 -34.12 <2e-16 ***
## Spawnyes 0.046300 0.003413 13.56 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Analisys by File
# Divide dataset by 'File'
split_spawnFile <- split(week_kuds, week_kuds$File)
# Function to use the wilcoxon test in each sub-dataframe
wilcox_test_kud95 <- function(data) {
# Verify if Spawn has exactly 2 levels (yes and no)
if(length(unique(data$Spawn)) == 2) {
test_result <- wilcox.test(KUD95 ~ Spawn, data = data)
return(test_result$p.value)
} else {
return(NA) # Return NA if not
}
}
# Apply the function and get the p-values
p_values <- lapply(split_spawnFile, wilcox_test_kud95)
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
# Exhibit p-values
print(p_values)
## $Dactylopterus_volitans
## [1] NA
##
## $Dentex_dentex1
## [1] 0.01030334
##
## $Dentex_dentex2
## [1] 0.1078085
##
## $Dicentrarchus_labrax1
## [1] 1.450456e-05
##
## $Dicentrarchus_labrax2
## [1] 0.0001058427
##
## $Diplodus_cervinus
## [1] 0.7975494
##
## $Diplodus_sargus1
## [1] 0.1054672
##
## $Diplodus_sargus2
## [1] 0.00252126
##
## $Diplodus_sargus3
## [1] 0.002851269
##
## $Diplodus_sargus4
## [1] 0.224059
##
## $Diplodus_sargus5
## [1] 0.2519681
##
## $Diplodus_sargus6
## [1] 0.6146928
##
## $Diplodus_vulgaris1
## [1] 0.1348518
##
## $Diplodus_vulgaris2
## [1] 0.3333333
##
## $Epinephelus_marginatus1
## [1] 0.08732378
##
## $Epinephelus_marginatus2
## [1] 0.8344767
##
## $Epinephelus_marginatus3
## [1] 0.2898365
##
## $Epinephelus_marginatus4
## [1] 2.725643e-05
##
## $Gadus_morhua1
## [1] 3.51374e-06
##
## $Gadus_morhua2
## [1] 0.3779574
##
## $Gadus_morhua3
## [1] 3.646825e-09
##
## $Labrus_bergylta
## [1] 1.611826e-07
##
## $Lichia_amia
## [1] 0.03284102
##
## $Lithognathus_mormyrus
## [1] NA
##
## $Pagellus_erythrinus
## [1] NA
##
## $Pagrus_pagrus1
## [1] 0.1970555
##
## $Pagrus_pagrus2
## [1] 0.3868507
##
## $Pomatomus_saltatrix
## [1] 0.90633
##
## $Pseudocaranx_dentex
## [1] 0.074285
##
## $Sciaena_umbra1
## [1] 0.100855
##
## $Sciaena_umbra2
## [1] 0.0530303
##
## $Scorpaena_porcus
## [1] 0.02050939
##
## $Scorpaena_scrofa1
## [1] 0.5053349
##
## $Scorpaena_scrofa2
## [1] 4.234985e-05
##
## $Seriola_dumerili
## [1] 2.94866e-12
##
## $Seriola_rivoliana
## [1] 4.398831e-06
##
## $Serranus_atricauda
## [1] 6.274069e-08
##
## $Serranus_cabrilla
## [1] NA
##
## $Serranus_scriba
## [1] 0.8412007
##
## $Solea_senegalensis
## [1] 0.03581104
##
## $Sparisoma_cretense
## [1] 0.3443516
##
## $Sparus_aurata1
## [1] 0.434566
##
## $Sparus_aurata2
## [1] 0.1317029
##
## $Sphyraena_viridensis1
## [1] 0.0005515177
##
## $Sphyraena_viridensis2
## [1] 2.323167e-15
##
## $Spondyliosoma_cantharus
## [1] 5.783145e-05
##
## $Umbrina_cirrosa
## [1] NA
##
## $Xyrichtys_novacula
## [1] NA
p_values<- t(as.data.frame(p_values))
# Function to use the wilcoxon test in each sub-dataframe
wilcox_test_kud50 <- function(data) {
# Verify if Spawn has exactly 2 levels (yes and no)
if(length(unique(data$Spawn)) == 2) {
test_result <- wilcox.test(KUD50 ~ Spawn, data = data)
return(test_result$p.value)
} else {
return(NA) # Return NA if not
}
}
# Apply the function and get the p-values
p_values <- lapply(split_spawnFile, wilcox_test_kud50)
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties
# Exhibit p-values
print(p_values)
## $Dactylopterus_volitans
## [1] NA
##
## $Dentex_dentex1
## [1] 0.0006766402
##
## $Dentex_dentex2
## [1] 0.07389496
##
## $Dicentrarchus_labrax1
## [1] 0.0007138528
##
## $Dicentrarchus_labrax2
## [1] 0.002072089
##
## $Diplodus_cervinus
## [1] 0.1787112
##
## $Diplodus_sargus1
## [1] 0.05129045
##
## $Diplodus_sargus2
## [1] 0.009404127
##
## $Diplodus_sargus3
## [1] 0.005536751
##
## $Diplodus_sargus4
## [1] 0.08955937
##
## $Diplodus_sargus5
## [1] 0.1154605
##
## $Diplodus_sargus6
## [1] 0.8877892
##
## $Diplodus_vulgaris1
## [1] 0.09801128
##
## $Diplodus_vulgaris2
## [1] 0.3333333
##
## $Epinephelus_marginatus1
## [1] 0.0007550945
##
## $Epinephelus_marginatus2
## [1] 0.8899593
##
## $Epinephelus_marginatus3
## [1] 0.360626
##
## $Epinephelus_marginatus4
## [1] 1.858381e-05
##
## $Gadus_morhua1
## [1] 8.959164e-05
##
## $Gadus_morhua2
## [1] 0.6036561
##
## $Gadus_morhua3
## [1] 4.300043e-09
##
## $Labrus_bergylta
## [1] 1.68144e-08
##
## $Lichia_amia
## [1] 0.2620757
##
## $Lithognathus_mormyrus
## [1] NA
##
## $Pagellus_erythrinus
## [1] NA
##
## $Pagrus_pagrus1
## [1] 0.04727474
##
## $Pagrus_pagrus2
## [1] 0.2663605
##
## $Pomatomus_saltatrix
## [1] 0.9564324
##
## $Pseudocaranx_dentex
## [1] 0.06130092
##
## $Sciaena_umbra1
## [1] 0.1403615
##
## $Sciaena_umbra2
## [1] 0.259324
##
## $Scorpaena_porcus
## [1] 0.01589705
##
## $Scorpaena_scrofa1
## [1] 0.8384118
##
## $Scorpaena_scrofa2
## [1] 0.0001346187
##
## $Seriola_dumerili
## [1] 7.89286e-12
##
## $Seriola_rivoliana
## [1] 0.1443612
##
## $Serranus_atricauda
## [1] 1.429118e-09
##
## $Serranus_cabrilla
## [1] NA
##
## $Serranus_scriba
## [1] 0.8428269
##
## $Solea_senegalensis
## [1] 0.1996681
##
## $Sparisoma_cretense
## [1] 0.6576976
##
## $Sparus_aurata1
## [1] 0.6422533
##
## $Sparus_aurata2
## [1] 0.005342033
##
## $Sphyraena_viridensis1
## [1] 0.001669869
##
## $Sphyraena_viridensis2
## [1] 6.294566e-07
##
## $Spondyliosoma_cantharus
## [1] 0.0001120269
##
## $Umbrina_cirrosa
## [1] NA
##
## $Xyrichtys_novacula
## [1] NA
p_values<- t(as.data.frame(p_values))
#Glmm KUD95 for each File
data_dentex_dentex1 <- subset(week_kuds, File == "Dentex_dentex1")
glmm_dentex_dentex1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dentex_dentex1, family = Gamma(link="log"))
summary(glmm_dentex_dentex1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex1
##
## AIC BIC logLik deviance df.resid
## 3.3 21.9 2.4 -4.7 774
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.03892 0.1973
## Number of obs: 778, groups: Transmitter, 19
##
## Dispersion estimate for Gamma family (sigma^2): 0.0384
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.11417 0.04733 2.412 0.0159 *
## Spawnyes 0.06093 0.01442 4.227 2.37e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex1$KUD95 ~ data_dentex_dentex1$Spawn)
#################################################################################
data_dentex_dentex2 <- subset(week_kuds, File == "Dentex_dentex2")
glmm_dentex_dentex2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dentex_dentex2, family = Gamma(link="log"))
summary(glmm_dentex_dentex2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex2
##
## AIC BIC logLik deviance df.resid
## 1008.2 1025.8 -500.1 1000.2 595
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1173 0.3426
## Number of obs: 599, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.138
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.28149 0.09143 3.079 0.00208 **
## Spawnyes 0.15458 0.03177 4.865 1.14e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex2$KUD95 ~ data_dentex_dentex2$Spawn)
#################################################################################
data_dicentrarchus_labrax1 <- subset(week_kuds, File == "Dicentrarchus_labrax1")
glmm_dicentrarchus_labrax1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax1, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax1
##
## AIC BIC logLik deviance df.resid
## 753.4 772.4 -372.7 745.4 850
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1006 0.3171
## Number of obs: 854, groups: Transmitter, 93
##
## Dispersion estimate for Gamma family (sigma^2): 0.0931
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.16255 0.03588 4.531 5.88e-06 ***
## Spawnyes -0.04691 0.06031 -0.778 0.437
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax1$KUD95 ~ data_dicentrarchus_labrax1$Spawn)
#################################################################################
data_dicentrarchus_labrax2 <- subset(week_kuds, File == "Dicentrarchus_labrax2")
glmm_dicentrarchus_labrax2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax2, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax2
##
## AIC BIC logLik deviance df.resid
## 1620.9 1638.7 -806.4 1612.9 633
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.2229 0.4722
## Number of obs: 637, groups: Transmitter, 28
##
## Dispersion estimate for Gamma family (sigma^2): 0.188
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.62269 0.09675 6.436 1.22e-10 ***
## Spawnyes 0.26338 0.03995 6.593 4.31e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax2$KUD95 ~ data_dicentrarchus_labrax2$Spawn)
#################################################################################
data_diplodus_cervinus <- subset(week_kuds, File == "Diplodus_cervinus")
glmm_diplodus_cervinus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_cervinus, family = Gamma(link="log"))
summary(glmm_diplodus_cervinus)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_cervinus
##
## AIC BIC logLik deviance df.resid
## 164.8 175.0 -78.4 156.8 90
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.147 0.3834
## Number of obs: 94, groups: Transmitter, 4
##
## Dispersion estimate for Gamma family (sigma^2): 0.15
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.31443 0.20324 1.547 0.12185
## Spawnyes -0.25631 0.09128 -2.808 0.00498 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_cervinus$KUD95 ~ data_diplodus_cervinus$Spawn)
#################################################################################
data_diplodus_sargus1 <- subset(week_kuds, File == "Diplodus_sargus1")
glmm_diplodus_sargus1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus1, family = Gamma(link="log"))
summary(glmm_diplodus_sargus1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus1
##
## AIC BIC logLik deviance df.resid
## 143.7 159.1 -67.8 135.7 347
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07295 0.2701
## Number of obs: 351, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.0723
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.042330 0.074203 0.571 0.568
## Spawnyes -0.009141 0.033723 -0.271 0.786
boxplot(data_diplodus_sargus1$KUD95 ~ data_diplodus_sargus1$Spawn)
#################################################################################
data_diplodus_sargus2 <- subset(week_kuds, File == "Diplodus_sargus2")
glmm_diplodus_sargus2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus2, family = Gamma(link="log"))
summary(glmm_diplodus_sargus2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus2
##
## AIC BIC logLik deviance df.resid
## -653.2 -635.2 330.6 -661.2 656
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.01889 0.1374
## Number of obs: 660, groups: Transmitter, 20
##
## Dispersion estimate for Gamma family (sigma^2): 0.0237
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.02555 0.03412 0.749 0.4539
## Spawnyes -0.02520 0.01480 -1.703 0.0886 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus2$KUD95 ~ data_diplodus_sargus2$Spawn)
#################################################################################
data_diplodus_sargus3 <- subset(week_kuds, File == "Diplodus_sargus3")
glmm_diplodus_sargus3 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus3, family = Gamma(link="log"))
summary(glmm_diplodus_sargus3)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus3
##
## AIC BIC logLik deviance df.resid
## -177.8 -168.2 92.9 -185.8 76
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0006858 0.02619
## Number of obs: 80, groups: Transmitter, 4
##
## Dispersion estimate for Gamma family (sigma^2): 0.00797
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.22252 0.01878 -11.851 < 2e-16 ***
## Spawnyes 0.08364 0.02016 4.148 3.36e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus3$KUD95 ~ data_diplodus_sargus3$Spawn)
#################################################################################
data_diplodus_sargus4 <- subset(week_kuds, File == "Diplodus_sargus4")
glmm_diplodus_sargus4 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus4, family = Gamma(link="log"))
summary(glmm_diplodus_sargus4)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus4
##
## AIC BIC logLik deviance df.resid
## -1780.1 -1758.9 894.1 -1788.1 1470
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02426 0.1558
## Number of obs: 1474, groups: Transmitter, 41
##
## Dispersion estimate for Gamma family (sigma^2): 0.0172
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.055328 0.025343 -2.183 0.029 *
## Spawnyes -0.004623 0.006936 -0.666 0.505
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus4$KUD95 ~ data_diplodus_sargus4$Spawn)
#################################################################################
data_diplodus_sargus5 <- subset(week_kuds, File == "Diplodus_sargus5")
glmm_diplodus_sargus5 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus5, family = Gamma(link="log"))
summary(glmm_diplodus_sargus5)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus5
##
## AIC BIC logLik deviance df.resid
## 291.3 311.3 -141.6 283.3 1098
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02454 0.1566
## Number of obs: 1102, groups: Transmitter, 73
##
## Dispersion estimate for Gamma family (sigma^2): 0.0703
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.03582 0.02336 1.533 0.125
## Spawnyes -0.01763 0.01790 -0.985 0.325
boxplot(data_diplodus_sargus5$KUD95 ~ data_diplodus_sargus5$Spawn)
#################################################################################
data_diplodus_sargus6 <- subset(week_kuds, File == "Diplodus_sargus6")
glmm_diplodus_sargus6 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus6, family = Gamma(link="log"))
summary(glmm_diplodus_sargus6)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus6
##
## AIC BIC logLik deviance df.resid
## -37.4 -30.5 22.7 -45.4 37
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1828 0.4275
## Number of obs: 41, groups: Transmitter, 6
##
## Dispersion estimate for Gamma family (sigma^2): 0.0165
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.06001 0.17875 0.336 0.737
## Spawnyes -0.02549 0.05101 -0.500 0.617
boxplot(data_diplodus_sargus6$KUD95 ~ data_diplodus_sargus6$Spawn)
#################################################################################
data_diplodus_vulgaris1 <- subset(week_kuds, File == "Diplodus_vulgaris1")
glmm_diplodus_vulgaris1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris1, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris1
##
## AIC BIC logLik deviance df.resid
## -7.4 0.0 7.7 -15.4 42
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.03967 0.1992
## Number of obs: 46, groups: Transmitter, 9
##
## Dispersion estimate for Gamma family (sigma^2): 0.0369
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.0009159 0.0769672 0.012 0.9905
## Spawnyes -0.1871862 0.1024773 -1.827 0.0678 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris1$KUD95 ~ data_diplodus_vulgaris1$Spawn)
#################################################################################
data_diplodus_vulgaris2 <- subset(week_kuds, File == "Diplodus_vulgaris2")
glmm_diplodus_vulgaris2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris2, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris2
##
## AIC BIC logLik deviance df.resid
## -7.7 -10.1 7.8 -15.7 0
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.006177 0.07859
## Number of obs: 4, groups: Transmitter, 2
##
## Dispersion estimate for Gamma family (sigma^2): 7.18e-05
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.53980 0.05590 9.66 <2e-16 ***
## Spawnyes -0.72481 0.01044 -69.42 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris2$KUD95 ~ data_diplodus_vulgaris2$Spawn)
#################################################################################
data_epinephelus_marginatus1 <- subset(week_kuds, File == "Epinephelus_marginatus1")
glmm_epinephelus_marginatus1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus1, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus1
##
## AIC BIC logLik deviance df.resid
## -3878.8 -3856.3 1943.4 -3886.8 2051
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.004326 0.06577
## Number of obs: 2055, groups: Transmitter, 11
##
## Dispersion estimate for Gamma family (sigma^2): 0.0131
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.232142 0.020292 -11.440 < 2e-16 ***
## Spawnyes 0.035552 0.005102 6.969 3.2e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus1$KUD95 ~ data_epinephelus_marginatus1$Spawn)
#################################################################################
data_epinephelus_marginatus2 <- subset(week_kuds, File == "Epinephelus_marginatus2")
glmm_epinephelus_marginatus2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus2, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus2
##
## AIC BIC logLik deviance df.resid
## -1926.5 -1910.2 967.3 -1934.5 433
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 5.013e-05 0.00708
## Number of obs: 437, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.00114
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.260076 0.002685 -96.87 < 2e-16 ***
## Spawnyes 0.010431 0.003580 2.91 0.00358 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus2$KUD95 ~ data_epinephelus_marginatus2$Spawn)
#################################################################################
data_epinephelus_marginatus3 <- subset(week_kuds, File == "Epinephelus_marginatus3")
glmm_epinephelus_marginatus3 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus3, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus3)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus3
##
## AIC BIC logLik deviance df.resid
## -689.6 -675.9 348.8 -697.6 223
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0002354 0.01534
## Number of obs: 227, groups: Transmitter, 4
##
## Dispersion estimate for Gamma family (sigma^2): 0.00425
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.249606 0.010156 -24.576 < 2e-16 ***
## Spawnyes 0.023242 0.008761 2.653 0.00798 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus3$KUD95 ~ data_epinephelus_marginatus3$Spawn)
#################################################################################
data_epinephelus_marginatus4 <- subset(week_kuds, File == "Epinephelus_marginatus4")
glmm_epinephelus_marginatus4 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus4, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus4)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus4
##
## AIC BIC logLik deviance df.resid
## 120.0 134.7 -56.0 112.0 289
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1211 0.348
## Number of obs: 293, groups: Transmitter, 17
##
## Dispersion estimate for Gamma family (sigma^2): 0.0814
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.10990 0.08870 -1.239 0.215
## Spawnyes 0.17134 0.03597 4.763 1.9e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus4$KUD95 ~ data_epinephelus_marginatus4$Spawn)
#################################################################################
data_gadus_morhua1 <- subset(week_kuds, File == "Gadus_morhua1")
glmm_gadus_morhua1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_gadus_morhua1, family = Gamma(link="log"))
summary(glmm_gadus_morhua1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua1
##
## AIC BIC logLik deviance df.resid
## 307.2 328.8 -149.6 299.2 1631
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.03343 0.1828
## Number of obs: 1635, groups: Transmitter, 60
##
## Dispersion estimate for Gamma family (sigma^2): 0.0566
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.01418 0.02752 0.515 0.606
## Spawnyes 0.07521 0.01442 5.217 1.82e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua1$KUD95 ~ data_gadus_morhua1$Spawn)
#################################################################################
data_gadus_morhua2 <- subset(week_kuds, File == "Gadus_morhua2")
glmm_gadus_morhua2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_gadus_morhua2, family = Gamma(link="log"))
summary(glmm_gadus_morhua2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua2
##
## AIC BIC logLik deviance df.resid
## 197.1 217.2 -94.5 189.1 1132
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.04499 0.2121
## Number of obs: 1136, groups: Transmitter, 56
##
## Dispersion estimate for Gamma family (sigma^2): 0.0684
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.004647 0.037490 -0.124 0.901
## Spawnyes -0.014876 0.024961 -0.596 0.551
boxplot(data_gadus_morhua2$KUD95 ~ data_gadus_morhua2$Spawn)
#################################################################################
data_gadus_morhua3 <- subset(week_kuds, File == "Gadus_morhua3")
glmm_gadus_morhua3 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_gadus_morhua3, family = Gamma(link="log"))
summary(glmm_gadus_morhua3)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua3
##
## AIC BIC logLik deviance df.resid
## -741.7 -725.8 374.9 -749.7 395
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.004891 0.06994
## Number of obs: 399, groups: Transmitter, 29
##
## Dispersion estimate for Gamma family (sigma^2): 0.0111
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.17004 0.01833 -9.278 < 2e-16 ***
## Spawnyes 0.04018 0.01253 3.206 0.00135 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua3$KUD95 ~ data_gadus_morhua3$Spawn)
#################################################################################
data_labrus_bergylta <- subset(week_kuds, File == "Labrus_bergylta")
glmm_labrus_bergylta <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_labrus_bergylta, family = Gamma(link="log"))
summary(glmm_labrus_bergylta)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_labrus_bergylta
##
## AIC BIC logLik deviance df.resid
## -2897.4 -2878.7 1452.7 -2905.4 789
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.001729 0.04158
## Number of obs: 793, groups: Transmitter, 25
##
## Dispersion estimate for Gamma family (sigma^2): 0.00195
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.193686 0.008599 -22.523 < 2e-16 ***
## Spawnyes 0.022007 0.003162 6.959 3.42e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_labrus_bergylta$KUD95 ~ data_labrus_bergylta$Spawn)
#################################################################################
data_lichia_amia<- subset(week_kuds, File == "Lichia_amia")
glmm_lichia_amia <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_lichia_amia, family = Gamma(link="log"))
summary(glmm_lichia_amia)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_lichia_amia
##
## AIC BIC logLik deviance df.resid
## 87.5 92.8 -39.7 79.5 24
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 1.717e-11 4.144e-06
## Number of obs: 28, groups: Transmitter, 1
##
## Dispersion estimate for Gamma family (sigma^2): 0.0735
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.9167 0.1356 6.762 1.36e-11 ***
## Spawnyes 0.4826 0.1464 3.296 0.00098 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_lichia_amia$KUD95 ~ data_lichia_amia$Spawn)
#################################################################################
data_pagrus_pagrus1 <- subset(week_kuds, File == "Pagrus_pagrus1")
glmm_pagrus_pagrus1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus1, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus1
##
## AIC BIC logLik deviance df.resid
## -192.5 -174.8 100.3 -200.5 614
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05044 0.2246
## Number of obs: 618, groups: Transmitter, 20
##
## Dispersion estimate for Gamma family (sigma^2): 0.048
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.08623 0.05379 -1.603 0.10888
## Spawnyes 0.04813 0.01858 2.591 0.00957 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pagrus_pagrus1$KUD95 ~ data_pagrus_pagrus1$Spawn)
#################################################################################
data_pagrus_pagrus2 <- subset(week_kuds, File == "Pagrus_pagrus2")
glmm_pagrus_pagrus2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus2, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus2
##
## AIC BIC logLik deviance df.resid
## 17.3 23.6 -4.6 9.3 32
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.7809 0.8837
## Number of obs: 36, groups: Transmitter, 5
##
## Dispersion estimate for Gamma family (sigma^2): 0.0428
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.46922 0.40388 1.162 0.245
## Spawnyes 0.02786 0.07300 0.382 0.703
boxplot(data_pagrus_pagrus2$KUD95 ~ data_pagrus_pagrus2$Spawn)
#################################################################################
data_pomatomus_saltatrix <- subset(week_kuds, File == "Pomatomus_saltatrix")
glmm_pomatomus_saltatrix <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pomatomus_saltatrix, family = Gamma(link="log"))
summary(glmm_pomatomus_saltatrix)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pomatomus_saltatrix
##
## AIC BIC logLik deviance df.resid
## 622.8 635.1 -307.4 614.8 155
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05284 0.2299
## Number of obs: 159, groups: Transmitter, 8
##
## Dispersion estimate for Gamma family (sigma^2): 0.404
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.10622 0.10263 10.778 <2e-16 ***
## Spawnyes -0.05879 0.13605 -0.432 0.666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pomatomus_saltatrix$KUD95 ~ data_pomatomus_saltatrix$Spawn)
#################################################################################
data_pseudocaranx_dentex <- subset(week_kuds, File == "Pseudocaranx_dentex")
glmm_pseudocaranx_dentex <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_pseudocaranx_dentex, family = Gamma(link="log"))
summary(glmm_pseudocaranx_dentex)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_pseudocaranx_dentex
##
## AIC BIC logLik deviance df.resid
## 1718.0 1739.3 -855.0 1710.0 1523
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1472 0.3837
## Number of obs: 1527, groups: Transmitter, 31
##
## Dispersion estimate for Gamma family (sigma^2): 0.143
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.10946 0.07357 1.488 0.13681
## Spawnyes 0.06570 0.02074 3.168 0.00154 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pseudocaranx_dentex$KUD95 ~ data_pseudocaranx_dentex$Spawn)
#################################################################################
data_sciaena_umbra1 <- subset(week_kuds, File == "Sciaena_umbra1")
glmm_sciaena_umbra1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra1, family = Gamma(link="log"))
summary(glmm_sciaena_umbra1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra1
##
## AIC BIC logLik deviance df.resid
## -523.4 -512.0 265.7 -531.4 125
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.008098 0.08999
## Number of obs: 129, groups: Transmitter, 15
##
## Dispersion estimate for Gamma family (sigma^2): 0.000979
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.178216 0.024450 -7.289 3.12e-13 ***
## Spawnyes -0.009497 0.008092 -1.174 0.241
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra1$KUD95 ~ data_sciaena_umbra1$Spawn)
#################################################################################
data_sciaena_umbra2 <- subset(week_kuds, File == "Sciaena_umbra2")
glmm_sciaena_umbra2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra2, family = Gamma(link="log"))
summary(glmm_sciaena_umbra2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra2
##
## AIC BIC logLik deviance df.resid
## 16.2 18.8 -4.1 8.2 10
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 4.245e-12 2.06e-06
## Number of obs: 14, groups: Transmitter, 1
##
## Dispersion estimate for Gamma family (sigma^2): 0.0555
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.48445 0.08901 5.443 5.25e-08 ***
## Spawnyes -0.29053 0.12588 -2.308 0.021 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra2$KUD95 ~ data_sciaena_umbra2$Spawn)
#################################################################################
data_scorpaena_porcus <- subset(week_kuds, File == "Scorpaena_porcus")
glmm_scorpaena_porcus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_scorpaena_porcus, family = Gamma(link="log"))
summary(glmm_scorpaena_porcus)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_porcus
##
## AIC BIC logLik deviance df.resid
## -143.2 -135.4 75.6 -151.2 48
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0005034 0.02244
## Number of obs: 52, groups: Transmitter, 13
##
## Dispersion estimate for Gamma family (sigma^2): 0.00441
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.17716 0.01605 -11.038 <2e-16 ***
## Spawnyes -0.04611 0.02097 -2.199 0.0279 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_porcus$KUD95 ~ data_scorpaena_porcus$Spawn)
#################################################################################
data_scorpaena_scrofa1 <- subset(week_kuds, File == "Scorpaena_scrofa1")
glmm_scorpaena_scrofa1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa1, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa1
##
## AIC BIC logLik deviance df.resid
## -202.8 -194.5 105.4 -210.8 54
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.002972 0.05451
## Number of obs: 58, groups: Transmitter, 7
##
## Dispersion estimate for Gamma family (sigma^2): 0.0017
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.18855 0.02615 -7.210 5.61e-13 ***
## Spawnyes -0.01773 0.01702 -1.042 0.297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa1$KUD95 ~ data_scorpaena_scrofa1$Spawn)
#################################################################################
data_scorpaena_scrofa2 <- subset(week_kuds, File == "Scorpaena_scrofa2")
glmm_scorpaena_scrofa2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa2, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa2
##
## AIC BIC logLik deviance df.resid
## -361.8 -344.9 184.9 -369.8 504
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0222 0.149
## Number of obs: 508, groups: Transmitter, 11
##
## Dispersion estimate for Gamma family (sigma^2): 0.0308
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.22672 0.04739 -4.784 1.72e-06 ***
## Spawnyes 0.16755 0.01625 10.313 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa2$KUD95 ~ data_scorpaena_scrofa2$Spawn)
#################################################################################
data_seriola_dumerili <- subset(week_kuds, File == "Seriola_dumerili")
glmm_seriola_dumerili <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_seriola_dumerili, family = Gamma(link="log"))
summary(glmm_seriola_dumerili)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_dumerili
##
## AIC BIC logLik deviance df.resid
## 1039.4 1054.9 -515.7 1031.4 352
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.06693 0.2587
## Number of obs: 356, groups: Transmitter, 8
##
## Dispersion estimate for Gamma family (sigma^2): 0.2
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.67695 0.10122 6.688 2.26e-11 ***
## Spawnyes 0.42055 0.04952 8.493 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_dumerili$KUD95 ~ data_seriola_dumerili$Spawn)
#################################################################################
data_seriola_rivoliana <- subset(week_kuds, File == "Seriola_rivoliana")
glmm_seriola_rivoliana <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_seriola_rivoliana, family = Gamma(link="log"))
summary(glmm_seriola_rivoliana)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_rivoliana
##
## AIC BIC logLik deviance df.resid
## 1065.4 1089.2 -528.7 1057.4 2783
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.01212 0.1101
## Number of obs: 2787, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.0968
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.05972 0.02911 -2.052 0.0402 *
## Spawnyes 0.04774 0.01209 3.949 7.86e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_rivoliana$KUD95 ~ data_seriola_rivoliana$Spawn)
#################################################################################
data_serranus_atricauda <- subset(week_kuds, File == "Serranus_atricauda")
glmm_serranus_atricauda <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_serranus_atricauda, family = Gamma(link="log"))
summary(glmm_serranus_atricauda)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_atricauda
##
## AIC BIC logLik deviance df.resid
## -3445.6 -3427.7 1726.8 -3453.6 646
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 8.305e-05 0.009113
## Number of obs: 650, groups: Transmitter, 9
##
## Dispersion estimate for Gamma family (sigma^2): 0.000474
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.263748 0.003475 -75.89 <2e-16 ***
## Spawnyes -0.002961 0.001762 -1.68 0.0929 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_atricauda$KUD95 ~ data_serranus_atricauda$Spawn)
#################################################################################
data_serranus_scriba <- subset(week_kuds, File == "Serranus_scriba")
glmm_serranus_scriba <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_serranus_scriba, family = Gamma(link="log"))
summary(glmm_serranus_scriba)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_scriba
##
## AIC BIC logLik deviance df.resid
## -30.5 -25.3 19.3 -38.5 23
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02947 0.1717
## Number of obs: 27, groups: Transmitter, 10
##
## Dispersion estimate for Gamma family (sigma^2): 0.00803
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.116927 0.082518 -1.417 0.156
## Spawnyes -0.002848 0.063364 -0.045 0.964
boxplot(data_serranus_scriba$KUD95 ~ data_serranus_scriba$Spawn)
#################################################################################
data_solea_senegalensis <- subset(week_kuds, File == "Solea_senegalensis")
glmm_solea_senegalensis <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_solea_senegalensis, family = Gamma(link="log"))
summary(glmm_solea_senegalensis)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_solea_senegalensis
##
## AIC BIC logLik deviance df.resid
## -38.6 -24.7 23.3 -46.6 233
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05953 0.244
## Number of obs: 237, groups: Transmitter, 22
##
## Dispersion estimate for Gamma family (sigma^2): 0.0456
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.08365 0.05788 1.445 0.148
## Spawnyes -0.03383 0.03830 -0.883 0.377
boxplot(data_solea_senegalensis$KUD95 ~ data_solea_senegalensis$Spawn)
#################################################################################
data_sparisoma_cretense <- subset(week_kuds, File == "Sparisoma_cretense")
glmm_sparisoma_cretense <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sparisoma_cretense, family = Gamma(link="log"))
summary(glmm_sparisoma_cretense)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sparisoma_cretense
##
## AIC BIC logLik deviance df.resid
## -1031.2 -1012.6 519.6 -1039.2 765
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.01394 0.1181
## Number of obs: 769, groups: Transmitter, 10
##
## Dispersion estimate for Gamma family (sigma^2): 0.0195
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.117408 0.038447 -3.054 0.00226 **
## Spawnyes -0.002199 0.010253 -0.214 0.83017
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparisoma_cretense$KUD95 ~ data_sparisoma_cretense$Spawn)
#################################################################################
data_sparus_aurata1 <- subset(week_kuds, File == "Sparus_aurata1")
glmm_sparus_aurata1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sparus_aurata1, family = Gamma(link="log"))
summary(glmm_sparus_aurata1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata1
##
## AIC BIC logLik deviance df.resid
## -517.9 -506.5 262.9 -525.9 123
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.001269 0.03563
## Number of obs: 127, groups: Transmitter, 7
##
## Dispersion estimate for Gamma family (sigma^2): 0.00129
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2336301 0.0149273 -15.651 <2e-16 ***
## Spawnyes 0.0008848 0.0076277 0.116 0.908
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata1$KUD95 ~ data_sparus_aurata1$Spawn)
#################################################################################
data_sparus_aurata2 <- subset(week_kuds, File == "Sparus_aurata2")
glmm_sparus_aurata2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sparus_aurata2, family = Gamma(link="log"))
summary(glmm_sparus_aurata2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata2
##
## AIC BIC logLik deviance df.resid
## 1502.0 1522.2 -747.0 1494.0 1129
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.3298 0.5742
## Number of obs: 1133, groups: Transmitter, 43
##
## Dispersion estimate for Gamma family (sigma^2): 0.0997
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.60117 0.09069 6.629 3.37e-11 ***
## Spawnyes 0.02415 0.02409 1.002 0.316
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata2$KUD95 ~ data_sparus_aurata2$Spawn)
#################################################################################
data_sphyraena_viridensis1 <- subset(week_kuds, File == "Sphyraena_viridensis1")
glmm_sphyraena_viridensis1 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis1, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis1)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis1
##
## AIC BIC logLik deviance df.resid
## 159.8 180.5 -75.9 151.8 1294
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02137 0.1462
## Number of obs: 1298, groups: Transmitter, 13
##
## Dispersion estimate for Gamma family (sigma^2): 0.0778
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.053769 0.043035 -1.249 0.212
## Spawnyes 0.009959 0.015824 0.629 0.529
boxplot(data_sphyraena_viridensis1$KUD95 ~ data_sphyraena_viridensis1$Spawn)
#################################################################################
data_sphyraena_viridensis2 <- subset(week_kuds, File == "Sphyraena_viridensis2")
glmm_sphyraena_viridensis2 <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis2, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis2)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis2
##
## AIC BIC logLik deviance df.resid
## 1765.4 1782.7 -878.7 1757.4 554
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1546 0.3932
## Number of obs: 558, groups: Transmitter, 17
##
## Dispersion estimate for Gamma family (sigma^2): 0.277
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.51837 0.10258 5.053 4.34e-07 ***
## Spawnyes 0.56290 0.04677 12.037 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sphyraena_viridensis2$KUD95 ~ data_sphyraena_viridensis2$Spawn)
#################################################################################
data_spondyliosoma_cantharus <- subset(week_kuds, File == "Spondyliosoma_cantharus")
glmm_spondyliosoma_cantharus <- glmmTMB(KUD95 ~ Spawn + (1|Transmitter), data=data_spondyliosoma_cantharus, family = Gamma(link="log"))
summary(glmm_spondyliosoma_cantharus)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Transmitter)
## Data: data_spondyliosoma_cantharus
##
## AIC BIC logLik deviance df.resid
## 142.7 160.7 -67.4 134.7 659
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.05021 0.2241
## Number of obs: 663, groups: Transmitter, 21
##
## Dispersion estimate for Gamma family (sigma^2): 0.0657
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.07551 0.05403 1.398 0.16219
## Spawnyes -0.05671 0.02129 -2.664 0.00773 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_spondyliosoma_cantharus$KUD95 ~ data_spondyliosoma_cantharus$Spawn)
#################################################################################
#Glmm KUD50 for each File
data_dentex_dentex1 <- subset(week_kuds, File == "Dentex_dentex1")
glmm_dentex_dentex1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dentex_dentex1, family = Gamma(link="log"))
summary(glmm_dentex_dentex1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex1
##
## AIC BIC logLik deviance df.resid
## -2280.3 -2261.7 1144.1 -2288.3 774
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.04613 0.2148
## Number of obs: 778, groups: Transmitter, 19
##
## Dispersion estimate for Gamma family (sigma^2): 0.0446
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.43928 0.05150 -27.948 < 2e-16 ***
## Spawnyes 0.08437 0.01557 5.418 6.02e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex1$KUD50 ~ data_dentex_dentex1$Spawn)
#################################################################################
data_dentex_dentex2 <- subset(week_kuds, File == "Dentex_dentex2")
glmm_dentex_dentex2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dentex_dentex2, family = Gamma(link="log"))
summary(glmm_dentex_dentex2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dentex_dentex2
##
## AIC BIC logLik deviance df.resid
## -1086.3 -1068.7 547.2 -1094.3 595
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07066 0.2658
## Number of obs: 599, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.112
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.29424 0.07216 -17.935 <2e-16 ***
## Spawnyes 0.02110 0.02875 0.734 0.463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dentex_dentex2$KUD50 ~ data_dentex_dentex2$Spawn)
#################################################################################
data_dicentrarchus_labrax1 <- subset(week_kuds, File == "Dicentrarchus_labrax1")
glmm_dicentrarchus_labrax1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax1, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax1
##
## AIC BIC logLik deviance df.resid
## -2194.2 -2175.2 1101.1 -2202.2 850
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.07913 0.2813
## Number of obs: 854, groups: Transmitter, 93
##
## Dispersion estimate for Gamma family (sigma^2): 0.0766
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.46528 0.03192 -45.91 <2e-16 ***
## Spawnyes -0.01360 0.05476 -0.25 0.804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax1$KUD50 ~ data_dicentrarchus_labrax1$Spawn)
#################################################################################
data_dicentrarchus_labrax2 <- subset(week_kuds, File == "Dicentrarchus_labrax2")
glmm_dicentrarchus_labrax2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_dicentrarchus_labrax2, family = Gamma(link="log"))
summary(glmm_dicentrarchus_labrax2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_dicentrarchus_labrax2
##
## AIC BIC logLik deviance df.resid
## -534.6 -516.8 271.3 -542.6 633
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1997 0.4469
## Number of obs: 637, groups: Transmitter, 28
##
## Dispersion estimate for Gamma family (sigma^2): 0.179
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.00901 0.09191 -10.978 < 2e-16 ***
## Spawnyes 0.20774 0.03891 5.338 9.37e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_dicentrarchus_labrax2$KUD50 ~ data_dicentrarchus_labrax2$Spawn)
#################################################################################
data_diplodus_cervinus <- subset(week_kuds, File == "Diplodus_cervinus")
glmm_diplodus_cervinus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_cervinus, family = Gamma(link="log"))
summary(glmm_diplodus_cervinus)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_cervinus
##
## AIC BIC logLik deviance df.resid
## -184.9 -174.7 96.4 -192.9 90
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.1266 0.3558
## Number of obs: 94, groups: Transmitter, 4
##
## Dispersion estimate for Gamma family (sigma^2): 0.0816
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.28557 0.18486 -6.954 3.55e-12 ***
## Spawnyes -0.13142 0.06793 -1.935 0.053 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_cervinus$KUD50 ~ data_diplodus_cervinus$Spawn)
#################################################################################
data_diplodus_sargus1 <- subset(week_kuds, File == "Diplodus_sargus1")
glmm_diplodus_sargus1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus1, family = Gamma(link="log"))
summary(glmm_diplodus_sargus1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus1
##
## AIC BIC logLik deviance df.resid
## -1007.6 -992.1 507.8 -1015.6 347
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.06204 0.2491
## Number of obs: 351, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.0617
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.53034 0.06840 -22.372 <2e-16 ***
## Spawnyes 0.00321 0.03104 0.103 0.918
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus1$KUD50 ~ data_diplodus_sargus1$Spawn)
#################################################################################
data_diplodus_sargus2 <- subset(week_kuds, File == "Diplodus_sargus2")
glmm_diplodus_sargus2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus2, family = Gamma(link="log"))
summary(glmm_diplodus_sargus2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus2
##
## AIC BIC logLik deviance df.resid
## -2444.7 -2426.7 1226.4 -2452.7 656
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02645 0.1626
## Number of obs: 660, groups: Transmitter, 20
##
## Dispersion estimate for Gamma family (sigma^2): 0.0333
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.48767 0.04036 -36.86 <2e-16 ***
## Spawnyes -0.04083 0.01751 -2.33 0.0197 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus2$KUD50 ~ data_diplodus_sargus2$Spawn)
#################################################################################
data_diplodus_sargus3 <- subset(week_kuds, File == "Diplodus_sargus3")
glmm_diplodus_sargus3 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus3, family = Gamma(link="log"))
summary(glmm_diplodus_sargus3)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus3
##
## AIC BIC logLik deviance df.resid
## -403.3 -393.7 205.6 -411.3 76
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0006003 0.0245
## Number of obs: 80, groups: Transmitter, 4
##
## Dispersion estimate for Gamma family (sigma^2): 0.0104
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.76106 0.01966 -89.58 < 2e-16 ***
## Spawnyes 0.08940 0.02304 3.88 0.000104 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus3$KUD50 ~ data_diplodus_sargus3$Spawn)
#################################################################################
data_diplodus_sargus4 <- subset(week_kuds, File == "Diplodus_sargus4")
glmm_diplodus_sargus4 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus4, family = Gamma(link="log"))
summary(glmm_diplodus_sargus4)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus4
##
## AIC BIC logLik deviance df.resid
## -6143.6 -6122.4 3075.8 -6151.6 1470
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.03507 0.1873
## Number of obs: 1474, groups: Transmitter, 41
##
## Dispersion estimate for Gamma family (sigma^2): 0.0185
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.579419 0.030197 -52.30 <2e-16 ***
## Spawnyes 0.001954 0.007205 0.27 0.786
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus4$KUD50 ~ data_diplodus_sargus4$Spawn)
#################################################################################
data_diplodus_sargus5 <- subset(week_kuds, File == "Diplodus_sargus5")
glmm_diplodus_sargus5 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus5, family = Gamma(link="log"))
summary(glmm_diplodus_sargus5)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus5
##
## AIC BIC logLik deviance df.resid
## -3438.5 -3418.4 1723.2 -3446.5 1098
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02855 0.169
## Number of obs: 1102, groups: Transmitter, 73
##
## Dispersion estimate for Gamma family (sigma^2): 0.0532
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.56587 0.02359 -66.39 <2e-16 ***
## Spawnyes 0.03946 0.01575 2.51 0.0122 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus5$KUD50 ~ data_diplodus_sargus5$Spawn)
#################################################################################
data_diplodus_sargus6 <- subset(week_kuds, File == "Diplodus_sargus6")
glmm_diplodus_sargus6 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_sargus6, family = Gamma(link="log"))
summary(glmm_diplodus_sargus6)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_sargus6
##
## AIC BIC logLik deviance df.resid
## -223.1 -216.2 115.5 -231.1 37
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.08092 0.2845
## Number of obs: 41, groups: Transmitter, 6
##
## Dispersion estimate for Gamma family (sigma^2): 0.00378
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.60644 0.11761 -13.659 <2e-16 ***
## Spawnyes 0.03602 0.02452 1.469 0.142
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_sargus6$KUD50 ~ data_diplodus_sargus6$Spawn)
#################################################################################
data_diplodus_vulgaris1 <- subset(week_kuds, File == "Diplodus_vulgaris1")
glmm_diplodus_vulgaris1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris1, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris1
##
## AIC BIC logLik deviance df.resid
## -195.6 -188.3 101.8 -203.6 42
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.04232 0.2057
## Number of obs: 46, groups: Transmitter, 9
##
## Dispersion estimate for Gamma family (sigma^2): 0.0133
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.60890 0.07275 -22.115 <2e-16 ***
## Spawnyes -0.05592 0.06168 -0.907 0.365
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris1$KUD50 ~ data_diplodus_vulgaris1$Spawn)
#################################################################################
data_diplodus_vulgaris2 <- subset(week_kuds, File == "Diplodus_vulgaris2")
glmm_diplodus_vulgaris2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_diplodus_vulgaris2, family = Gamma(link="log"))
summary(glmm_diplodus_vulgaris2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_diplodus_vulgaris2
##
## AIC BIC logLik deviance df.resid
## -26.9 -29.3 17.4 -34.9 0
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.002723 0.05218
## Number of obs: 4, groups: Transmitter, 2
##
## Dispersion estimate for Gamma family (sigma^2): 9.14e-06
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.283298 0.036960 -34.72 <2e-16 ***
## Spawnyes -0.567608 0.003708 -153.06 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_diplodus_vulgaris2$KUD50 ~ data_diplodus_vulgaris2$Spawn)
#################################################################################
data_epinephelus_marginatus1 <- subset(week_kuds, File == "Epinephelus_marginatus1")
glmm_epinephelus_marginatus1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus1, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus1
##
## AIC BIC logLik deviance df.resid
## -10604.1 -10581.6 5306.0 -10612.1 2051
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.005139 0.07169
## Number of obs: 2055, groups: Transmitter, 11
##
## Dispersion estimate for Gamma family (sigma^2): 0.0109
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.767219 0.021966 -80.45 < 2e-16 ***
## Spawnyes 0.020935 0.004637 4.51 6.34e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus1$KUD50 ~ data_epinephelus_marginatus1$Spawn)
#################################################################################
data_epinephelus_marginatus2 <- subset(week_kuds, File == "Epinephelus_marginatus2")
glmm_epinephelus_marginatus2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus2, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus2
##
## AIC BIC logLik deviance df.resid
## -3693.3 -3677.0 1850.6 -3701.3 433
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 4.253e-05 0.006522
## Number of obs: 437, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.000423
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.794331 0.002075 -864.6 < 2e-16 ***
## Spawnyes 0.007375 0.002189 3.4 0.000755 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus2$KUD50 ~ data_epinephelus_marginatus2$Spawn)
#################################################################################
data_epinephelus_marginatus3 <- subset(week_kuds, File == "Epinephelus_marginatus3")
glmm_epinephelus_marginatus3 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus3, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus3)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus3
##
## AIC BIC logLik deviance df.resid
## -1368.5 -1354.8 688.2 -1376.5 223
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0002907 0.01705
## Number of obs: 227, groups: Transmitter, 4
##
## Dispersion estimate for Gamma family (sigma^2): 0.00461
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.785956 0.010985 -162.58 < 2e-16 ***
## Spawnyes 0.023495 0.009119 2.58 0.00999 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus3$KUD50 ~ data_epinephelus_marginatus3$Spawn)
#################################################################################
data_epinephelus_marginatus4 <- subset(week_kuds, File == "Epinephelus_marginatus4")
glmm_epinephelus_marginatus4 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_epinephelus_marginatus4, family = Gamma(link="log"))
summary(glmm_epinephelus_marginatus4)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_epinephelus_marginatus4
##
## AIC BIC logLik deviance df.resid
## -1033.4 -1018.7 520.7 -1041.4 289
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.06315 0.2513
## Number of obs: 293, groups: Transmitter, 17
##
## Dispersion estimate for Gamma family (sigma^2): 0.0367
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.67263 0.06362 -26.29 < 2e-16 ***
## Spawnyes 0.11494 0.02389 4.81 1.51e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_epinephelus_marginatus4$KUD50 ~ data_epinephelus_marginatus4$Spawn)
#################################################################################
data_gadus_morhua1 <- subset(week_kuds, File == "Gadus_morhua1")
glmm_gadus_morhua1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_gadus_morhua1, family = Gamma(link="log"))
summary(glmm_gadus_morhua1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua1
##
## AIC BIC logLik deviance df.resid
## -4354.4 -4332.8 2181.2 -4362.4 1631
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.04348 0.2085
## Number of obs: 1635, groups: Transmitter, 60
##
## Dispersion estimate for Gamma family (sigma^2): 0.0697
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.51844 0.03117 -48.72 < 2e-16 ***
## Spawnyes 0.08095 0.01598 5.07 4.08e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua1$KUD50 ~ data_gadus_morhua1$Spawn)
#################################################################################
data_gadus_morhua2 <- subset(week_kuds, File == "Gadus_morhua2")
glmm_gadus_morhua2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_gadus_morhua2, family = Gamma(link="log"))
summary(glmm_gadus_morhua2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua2
##
## AIC BIC logLik deviance df.resid
## -3787.9 -3767.8 1898.0 -3795.9 1132
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.03061 0.175
## Number of obs: 1136, groups: Transmitter, 56
##
## Dispersion estimate for Gamma family (sigma^2): 0.0486
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.57239 0.03120 -50.40 <2e-16 ***
## Spawnyes -0.04405 0.02104 -2.09 0.0363 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua2$KUD50 ~ data_gadus_morhua2$Spawn)
#################################################################################
data_gadus_morhua3 <- subset(week_kuds, File == "Gadus_morhua3")
glmm_gadus_morhua3 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_gadus_morhua3, family = Gamma(link="log"))
summary(glmm_gadus_morhua3)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_gadus_morhua3
##
## AIC BIC logLik deviance df.resid
## -1819.0 -1803.0 913.5 -1827.0 395
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.008528 0.09235
## Number of obs: 399, groups: Transmitter, 29
##
## Dispersion estimate for Gamma family (sigma^2): 0.0158
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.70272 0.02317 -73.47 < 2e-16 ***
## Spawnyes 0.05523 0.01494 3.70 0.000219 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_gadus_morhua3$KUD50 ~ data_gadus_morhua3$Spawn)
#################################################################################
data_labrus_bergylta <- subset(week_kuds, File == "Labrus_bergylta")
glmm_labrus_bergylta <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_labrus_bergylta, family = Gamma(link="log"))
summary(glmm_labrus_bergylta)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_labrus_bergylta
##
## AIC BIC logLik deviance df.resid
## -5170.0 -5151.2 2589.0 -5178.0 789
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.002124 0.04609
## Number of obs: 793, groups: Transmitter, 25
##
## Dispersion estimate for Gamma family (sigma^2): 0.00238
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.727118 0.009530 -181.23 < 2e-16 ***
## Spawnyes 0.024741 0.003492 7.08 1.39e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_labrus_bergylta$KUD50 ~ data_labrus_bergylta$Spawn)
#################################################################################
data_lichia_amia<- subset(week_kuds, File == "Lichia_amia")
glmm_lichia_amia <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_lichia_amia, family = Gamma(link="log"))
summary(glmm_lichia_amia)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_lichia_amia
##
## AIC BIC logLik deviance df.resid
## 5.2 10.5 1.4 -2.8 24
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 1.536e-12 1.239e-06
## Number of obs: 28, groups: Transmitter, 1
##
## Dispersion estimate for Gamma family (sigma^2): 0.109
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.5626 0.1650 -3.409 0.000652 ***
## Spawnyes 0.2794 0.1782 1.567 0.117030
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_lichia_amia$KUD50 ~ data_lichia_amia$Spawn)
#################################################################################
data_pagrus_pagrus1 <- subset(week_kuds, File == "Pagrus_pagrus1")
glmm_pagrus_pagrus1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus1, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus1
##
## AIC BIC logLik deviance df.resid
## -2241.5 -2223.8 1124.8 -2249.5 614
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02536 0.1593
## Number of obs: 618, groups: Transmitter, 20
##
## Dispersion estimate for Gamma family (sigma^2): 0.0413
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.66357 0.03941 -42.22 <2e-16 ***
## Spawnyes 0.03767 0.01717 2.19 0.0283 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pagrus_pagrus1$KUD50 ~ data_pagrus_pagrus1$Spawn)
#################################################################################
data_pagrus_pagrus2 <- subset(week_kuds, File == "Pagrus_pagrus2")
glmm_pagrus_pagrus2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pagrus_pagrus2, family = Gamma(link="log"))
summary(glmm_pagrus_pagrus2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pagrus_pagrus2
##
## AIC BIC logLik deviance df.resid
## -97.9 -91.6 53.0 -105.9 32
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.7335 0.8565
## Number of obs: 36, groups: Transmitter, 5
##
## Dispersion estimate for Gamma family (sigma^2): 0.0373
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.083803 0.390730 -2.774 0.00554 **
## Spawnyes 0.007209 0.068040 0.106 0.91562
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pagrus_pagrus2$KUD50 ~ data_pagrus_pagrus2$Spawn)
#################################################################################
data_pomatomus_saltatrix <- subset(week_kuds, File == "Pomatomus_saltatrix")
glmm_pomatomus_saltatrix <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pomatomus_saltatrix, family = Gamma(link="log"))
summary(glmm_pomatomus_saltatrix)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pomatomus_saltatrix
##
## AIC BIC logLik deviance df.resid
## 39.4 51.7 -15.7 31.4 155
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.06272 0.2504
## Number of obs: 159, groups: Transmitter, 8
##
## Dispersion estimate for Gamma family (sigma^2): 0.368
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.69788 0.10724 -6.507 7.64e-11 ***
## Spawnyes -0.06845 0.13018 -0.526 0.599
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pomatomus_saltatrix$KUD50 ~ data_pomatomus_saltatrix$Spawn)
#################################################################################
data_pseudocaranx_dentex <- subset(week_kuds, File == "Pseudocaranx_dentex")
glmm_pseudocaranx_dentex <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_pseudocaranx_dentex, family = Gamma(link="log"))
summary(glmm_pseudocaranx_dentex)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_pseudocaranx_dentex
##
## AIC BIC logLik deviance df.resid
## -3844.6 -3823.3 1926.3 -3852.6 1523
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.08244 0.2871
## Number of obs: 1527, groups: Transmitter, 31
##
## Dispersion estimate for Gamma family (sigma^2): 0.0955
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.53944 0.05554 -27.717 < 2e-16 ***
## Spawnyes 0.07720 0.01681 4.592 4.4e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_pseudocaranx_dentex$KUD50 ~ data_pseudocaranx_dentex$Spawn)
#################################################################################
data_sciaena_umbra1 <- subset(week_kuds, File == "Sciaena_umbra1")
glmm_sciaena_umbra1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra1, family = Gamma(link="log"))
summary(glmm_sciaena_umbra1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra1
##
## AIC BIC logLik deviance df.resid
## -889.6 -878.1 448.8 -897.6 125
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0109 0.1044
## Number of obs: 129, groups: Transmitter, 15
##
## Dispersion estimate for Gamma family (sigma^2): 0.00121
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.704016 0.028254 -60.31 <2e-16 ***
## Spawnyes -0.010727 0.009007 -1.19 0.234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra1$KUD50 ~ data_sciaena_umbra1$Spawn)
#################################################################################
data_sciaena_umbra2 <- subset(week_kuds, File == "Sciaena_umbra2")
glmm_sciaena_umbra2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sciaena_umbra2, family = Gamma(link="log"))
summary(glmm_sciaena_umbra2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sciaena_umbra2
##
## AIC BIC logLik deviance df.resid
## -33.4 -30.8 20.7 -41.4 10
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 1.673e-11 4.091e-06
## Number of obs: 14, groups: Transmitter, 1
##
## Dispersion estimate for Gamma family (sigma^2): 0.0396
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.16613 0.07522 -15.50 <2e-16 ***
## Spawnyes -0.20429 0.10638 -1.92 0.0548 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sciaena_umbra2$KUD50 ~ data_sciaena_umbra2$Spawn)
#################################################################################
data_scorpaena_porcus <- subset(week_kuds, File == "Scorpaena_porcus")
glmm_scorpaena_porcus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_scorpaena_porcus, family = Gamma(link="log"))
summary(glmm_scorpaena_porcus)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_porcus
##
## AIC BIC logLik deviance df.resid
## -282.9 -275.1 145.4 -290.9 48
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.0004771 0.02184
## Number of obs: 52, groups: Transmitter, 13
##
## Dispersion estimate for Gamma family (sigma^2): 0.00659
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.70114 0.01877 -90.63 <2e-16 ***
## Spawnyes -0.05518 0.02466 -2.24 0.0253 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_porcus$KUD50 ~ data_scorpaena_porcus$Spawn)
#################################################################################
data_scorpaena_scrofa1 <- subset(week_kuds, File == "Scorpaena_scrofa1")
glmm_scorpaena_scrofa1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa1, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa1
##
## AIC BIC logLik deviance df.resid
## -363.4 -355.2 185.7 -371.4 54
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.003692 0.06077
## Number of obs: 58, groups: Transmitter, 7
##
## Dispersion estimate for Gamma family (sigma^2): 0.00229
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.71056 0.02962 -57.75 <2e-16 ***
## Spawnyes -0.02598 0.01977 -1.31 0.189
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa1$KUD50 ~ data_scorpaena_scrofa1$Spawn)
#################################################################################
data_scorpaena_scrofa2 <- subset(week_kuds, File == "Scorpaena_scrofa2")
glmm_scorpaena_scrofa2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_scorpaena_scrofa2, family = Gamma(link="log"))
summary(glmm_scorpaena_scrofa2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_scorpaena_scrofa2
##
## AIC BIC logLik deviance df.resid
## -1820.4 -1803.5 914.2 -1828.4 504
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02351 0.1533
## Number of obs: 508, groups: Transmitter, 11
##
## Dispersion estimate for Gamma family (sigma^2): 0.0371
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.74615 0.04910 -35.57 <2e-16 ***
## Spawnyes 0.16387 0.01782 9.20 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_scorpaena_scrofa2$KUD50 ~ data_scorpaena_scrofa2$Spawn)
#################################################################################
data_seriola_dumerili <- subset(week_kuds, File == "Seriola_dumerili")
glmm_seriola_dumerili <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_seriola_dumerili, family = Gamma(link="log"))
summary(glmm_seriola_dumerili)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_dumerili
##
## AIC BIC logLik deviance df.resid
## -169.4 -153.9 88.7 -177.4 352
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.04089 0.2022
## Number of obs: 356, groups: Transmitter, 8
##
## Dispersion estimate for Gamma family (sigma^2): 0.202
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.02861 0.08266 -12.444 <2e-16 ***
## Spawnyes 0.42232 0.04948 8.535 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_dumerili$KUD50 ~ data_seriola_dumerili$Spawn)
#################################################################################
data_seriola_rivoliana <- subset(week_kuds, File == "Seriola_rivoliana")
glmm_seriola_rivoliana <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_seriola_rivoliana, family = Gamma(link="log"))
summary(glmm_seriola_rivoliana)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_seriola_rivoliana
##
## AIC BIC logLik deviance df.resid
## -9159.6 -9135.8 4583.8 -9167.6 2783
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.006496 0.0806
## Number of obs: 2787, groups: Transmitter, 16
##
## Dispersion estimate for Gamma family (sigma^2): 0.0613
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.667018 0.021514 -77.49 < 2e-16 ***
## Spawnyes 0.028314 0.009618 2.94 0.00324 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_seriola_rivoliana$KUD50 ~ data_seriola_rivoliana$Spawn)
#################################################################################
data_serranus_atricauda <- subset(week_kuds, File == "Serranus_atricauda")
glmm_serranus_atricauda <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_serranus_atricauda, family = Gamma(link="log"))
summary(glmm_serranus_atricauda)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_atricauda
##
## AIC BIC logLik deviance df.resid
## -5402.6 -5384.7 2705.3 -5410.6 646
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 9.271e-05 0.009629
## Number of obs: 650, groups: Transmitter, 9
##
## Dispersion estimate for Gamma family (sigma^2): 0.000501
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.796127 0.003653 -491.7 <2e-16 ***
## Spawnyes -0.004663 0.001812 -2.6 0.0101 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_atricauda$KUD50 ~ data_serranus_atricauda$Spawn)
#################################################################################
data_serranus_scriba <- subset(week_kuds, File == "Serranus_scriba")
glmm_serranus_scriba <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_serranus_scriba, family = Gamma(link="log"))
summary(glmm_serranus_scriba)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_serranus_scriba
##
## AIC BIC logLik deviance df.resid
## -111.0 -105.8 59.5 -119.0 23
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02159 0.1469
## Number of obs: 27, groups: Transmitter, 10
##
## Dispersion estimate for Gamma family (sigma^2): 0.0111
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.72592 0.08615 -20.033 <2e-16 ***
## Spawnyes 0.06062 0.07423 0.817 0.414
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_serranus_scriba$KUD50 ~ data_serranus_scriba$Spawn)
#################################################################################
data_solea_senegalensis <- subset(week_kuds, File == "Solea_senegalensis")
glmm_solea_senegalensis <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_solea_senegalensis, family = Gamma(link="log"))
summary(glmm_solea_senegalensis)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_solea_senegalensis
##
## AIC BIC logLik deviance df.resid
## -794.3 -780.5 401.2 -802.3 233
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.04342 0.2084
## Number of obs: 237, groups: Transmitter, 22
##
## Dispersion estimate for Gamma family (sigma^2): 0.0426
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.49352 0.05066 -29.481 <2e-16 ***
## Spawnyes -0.03657 0.03680 -0.994 0.32
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_solea_senegalensis$KUD50 ~ data_solea_senegalensis$Spawn)
#################################################################################
data_sparisoma_cretense <- subset(week_kuds, File == "Sparisoma_cretense")
glmm_sparisoma_cretense <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sparisoma_cretense, family = Gamma(link="log"))
summary(glmm_sparisoma_cretense)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sparisoma_cretense
##
## AIC BIC logLik deviance df.resid
## -3587.9 -3569.3 1797.9 -3595.9 765
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.02709 0.1646
## Number of obs: 769, groups: Transmitter, 10
##
## Dispersion estimate for Gamma family (sigma^2): 0.0153
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.662623 0.052688 -31.556 <2e-16 ***
## Spawnyes 0.004549 0.009085 0.501 0.617
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparisoma_cretense$KUD50 ~ data_sparisoma_cretense$Spawn)
#################################################################################
data_sparus_aurata1 <- subset(week_kuds, File == "Sparus_aurata1")
glmm_sparus_aurata1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sparus_aurata1, family = Gamma(link="log"))
summary(glmm_sparus_aurata1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata1
##
## AIC BIC logLik deviance df.resid
## -854.6 -843.2 431.3 -862.6 123
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.001468 0.03831
## Number of obs: 127, groups: Transmitter, 7
##
## Dispersion estimate for Gamma family (sigma^2): 0.00199
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.768618 0.016448 -107.53 <2e-16 ***
## Spawnyes 0.003994 0.009427 0.42 0.672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata1$KUD50 ~ data_sparus_aurata1$Spawn)
#################################################################################
data_sparus_aurata2 <- subset(week_kuds, File == "Sparus_aurata2")
glmm_sparus_aurata2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sparus_aurata2, family = Gamma(link="log"))
summary(glmm_sparus_aurata2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sparus_aurata2
##
## AIC BIC logLik deviance df.resid
## -2100.7 -2080.6 1054.3 -2108.7 1129
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.2786 0.5279
## Number of obs: 1133, groups: Transmitter, 43
##
## Dispersion estimate for Gamma family (sigma^2): 0.0996
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.01828 0.08383 -12.147 <2e-16 ***
## Spawnyes 0.05459 0.02412 2.264 0.0236 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sparus_aurata2$KUD50 ~ data_sparus_aurata2$Spawn)
#################################################################################
data_sphyraena_viridensis1 <- subset(week_kuds, File == "Sphyraena_viridensis1")
glmm_sphyraena_viridensis1 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis1, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis1)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis1
##
## AIC BIC logLik deviance df.resid
## -4622.5 -4601.8 2315.2 -4630.5 1294
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.009475 0.09734
## Number of obs: 1298, groups: Transmitter, 13
##
## Dispersion estimate for Gamma family (sigma^2): 0.0478
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.68092 0.02922 -57.53 <2e-16 ***
## Spawnyes 0.02217 0.01240 1.79 0.0739 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sphyraena_viridensis1$KUD50 ~ data_sphyraena_viridensis1$Spawn)
#################################################################################
data_sphyraena_viridensis2 <- subset(week_kuds, File == "Sphyraena_viridensis2")
glmm_sphyraena_viridensis2 <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_sphyraena_viridensis2, family = Gamma(link="log"))
summary(glmm_sphyraena_viridensis2)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_sphyraena_viridensis2
##
## AIC BIC logLik deviance df.resid
## -292.5 -275.2 150.2 -300.5 554
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.09615 0.3101
## Number of obs: 558, groups: Transmitter, 17
##
## Dispersion estimate for Gamma family (sigma^2): 0.219
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.06979 0.08232 -12.995 < 2e-16 ***
## Spawnyes 0.27259 0.04106 6.639 3.16e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_sphyraena_viridensis2$KUD50 ~ data_sphyraena_viridensis2$Spawn)
#################################################################################
data_spondyliosoma_cantharus <- subset(week_kuds, File == "Spondyliosoma_cantharus")
glmm_spondyliosoma_cantharus <- glmmTMB(KUD50 ~ Spawn + (1|Transmitter), data=data_spondyliosoma_cantharus, family = Gamma(link="log"))
summary(glmm_spondyliosoma_cantharus)
## Family: Gamma ( log )
## Formula: KUD50 ~ Spawn + (1 | Transmitter)
## Data: data_spondyliosoma_cantharus
##
## AIC BIC logLik deviance df.resid
## -1988.4 -1970.4 998.2 -1996.4 659
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Transmitter (Intercept) 0.06214 0.2493
## Number of obs: 663, groups: Transmitter, 21
##
## Dispersion estimate for Gamma family (sigma^2): 0.0586
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.46186 0.05876 -24.877 < 2e-16 ***
## Spawnyes -0.05414 0.02015 -2.687 0.00721 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(data_spondyliosoma_cantharus$KUD50 ~ data_spondyliosoma_cantharus$Spawn)
#################################################################################
#Exploratory analysis
plot(week_kuds$Week, week_kuds$KUD95)
glm_week <- glm(KUD95 ~ Week, data = week_kuds, family=Gamma(link="log"))
with(week_kuds, plot(KUD95 ~ Week, pch = 1, col="deepskyblue"))
seq <- levels(week_kuds$Week)
predictweek <- predict(glm_week,newdata=data.frame(Week=seq), type="response")
lines(seq, predictweek, lty=1, col="red")
## Warning in xy.coords(x, y): NAs introduced by coercion
week_kuds$Week <- as.numeric(week_kuds$Week)
##See how KUD varies over Weeks by Spawning season
week_kuds_ss <- subset(week_kuds, SpawnSeason == "SS")
week_kuds_a <- subset(week_kuds, SpawnSeason == "A")
week_kuds_w <- subset(week_kuds, SpawnSeason == "W")
grid.arrange(
ggplot(week_kuds_ss, aes(x = Week, y = KUD95)) +
geom_point(col = "green") +
labs(title = "KUD95 over Weeks SS",
x = "Week",
y = "KUD95") +
scale_y_continuous(limits = c(0, 15)) +
theme_minimal() ,
ggplot(week_kuds_a, aes(x = Week, y = KUD95)) +
geom_point(col = "red") +
labs(title = "KUD95 over Weeks A",
x = "Week",
y = "KUD95") +
scale_y_continuous(limits = c(0, 15)) +
theme_minimal() ,
ggplot(week_kuds_w, aes(x = Week, y = KUD95)) +
geom_point(col = "blue") +
labs(title = "KUD95 over Weeks W",
x = "Week",
y = "KUD95") +
scale_y_continuous(limits = c(0, 15)) +
theme_minimal()
)
#With predictions
#SS
gam_ss <- gam(KUD95 ~ s(Week), data = week_kuds_ss[week_kuds_ss$SpawnSeason == "SS", ], family = Gamma(link = "log"))
summary(gam_ss)
##
## Family: Gamma
## Link function: log
##
## Formula:
## KUD95 ~ s(Week)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.065008 0.004294 15.14 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(Week) 7.774 8.451 14.3 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0049 Deviance explained = 1.2%
## GCV = 0.18719 Scale est. = 0.41752 n = 22642
week_kuds_ss$predicted <- predict(gam_ss, newdata = week_kuds_ss, type = "response")
plotss<- ggplot(week_kuds_ss, aes(x = Week, y = KUD95)) +
geom_point(col = "green") +
geom_line(aes(y = predicted), col = "black", linetype = "dashed") +
labs(title = "KUD95 over Weeks (SpawnSeason = SS)",
x = "Week",
y = "KUD95") +
scale_y_continuous(limits = c(0, 15)) +
theme_minimal()
#SA
gam_a <- gam(KUD95 ~ s(Week), data = week_kuds_a[week_kuds_a$SpawnSeason == "A", ], family = Gamma(link = "log"))
summary(gam_a)
##
## Family: Gamma
## Link function: log
##
## Formula:
## KUD95 ~ s(Week)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.37619 0.01799 20.91 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(Week) 4.532 5.558 1.304 0.252
##
## R-sq.(adj) = 0.00297 Deviance explained = 1.1%
## GCV = 0.26384 Scale est. = 0.43226 n = 1335
week_kuds_a$predicted <- predict(gam_a, newdata = week_kuds_a, type = "response")
plota <- ggplot(week_kuds_a, aes(x = Week, y = KUD95)) +
geom_point(col = "red") +
geom_line(aes(y = predicted), col = "black", linetype = "dashed") +
labs(title = "KUD95 over Weeks (SpawnSeason = A)",
x = "Week",
y = "KUD95") +
scale_y_continuous(limits = c(0, 15)) +
theme_minimal()
#W
gam_w <- gam(KUD95 ~ s(Week), data = week_kuds_w[week_kuds_w$SpawnSeason == "W", ], family = Gamma(link = "log"))
summary(gam_w)
##
## Family: Gamma
## Link function: log
##
## Formula:
## KUD95 ~ s(Week)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.43817 0.01598 27.42 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(Week) 8.48 8.923 34.47 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.131 Deviance explained = 21%
## GCV = 0.31676 Scale est. = 0.4175 n = 1635
week_kuds_w$predicted <- predict(gam_a, newdata = week_kuds_w, type = "response")
plotw <- ggplot(week_kuds_w, aes(x = Week, y = KUD95)) +
geom_point(col = "blue") +
geom_line(aes(y = predicted), col = "black", linetype = "dashed") +
labs(title = "KUD95 over Weeks (SpawnSeason = w)",
x = "Week",
y = "KUD95") +
scale_y_continuous(limits = c(0, 15)) +
theme_minimal()
grid.arrange(plotss, plota, plotw)
##########################################################################################################
week_kuds$Week <- as.numeric(week_kuds$Week)
#Model that describes KUD95 over Week by Spawning season
gam_model <- gam(KUD95 ~ s(Week, by = SpawnSeason), data = week_kuds, family = Gamma(link = "log"))
summary(gam_model)
##
## Family: Gamma
## Link function: log
##
## Formula:
## KUD95 ~ s(Week, by = SpawnSeason)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.10958 0.00418 26.21 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(Week):SpawnSeasonA 6.476 7.311 7.661 <2e-16 ***
## s(Week):SpawnSeasonSS 7.661 8.374 13.125 <2e-16 ***
## s(Week):SpawnSeasonW 8.373 8.649 28.859 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0293 Deviance explained = 4.29%
## GCV = 0.21295 Scale est. = 0.44276 n = 25612
#Make predictions of the model
new_data <- data.frame(Week = rep(seq(min(week_kuds$Week), max(week_kuds$Week), length.out = 100), times = nlevels(week_kuds$SpawnSeason)),
SpawnSeason = factor(rep(levels(week_kuds$SpawnSeason), each = 100)))
new_data$predicted_KUD95 <- predict(gam_model, new_data, type = "response")
ggplot(new_data, aes(x = Week, y = predicted_KUD95, color = SpawnSeason)) +
geom_line() +
labs(title = "Predicted KUD95 over Weeks by Spawning Season",
x = "Week",
y = "Predicted KUD95") +
scale_y_continuous(limits = c(0, 15)) +
theme_minimal()
week_kuds$Week <- as.factor(week_kuds$Week)
plot(week_kuds$Week, week_kuds$KUD50)
glm_week1 <- glm(KUD50 ~ Week, data = week_kuds, family=Gamma(link="log"))
with(week_kuds, plot(KUD50 ~ Week, pch = 1, col="deepskyblue"))
seq1 <- levels(week_kuds$Week)
predictweek1 <- predict(glm_week1,newdata=data.frame(Week=seq1), type="response")
lines(seq1, predictweek1, lty=1, col="red")
glmm_spawn <- glmmTMB(KUD95 ~ Spawn + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(glmm_spawn)
## Family: Gamma ( log )
## Formula: KUD95 ~ Spawn + (1 | Species)
## Data: week_kuds
##
## AIC BIC logLik deviance df.resid
## 25460.0 25492.6 -12726.0 25452.0 25608
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Species (Intercept) 0.1492 0.3862
## Number of obs: 25612, groups: Species, 30
##
## Dispersion estimate for Gamma family (sigma^2): 0.148
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.122603 0.071420 1.717 0.086 .
## Spawnyes 0.065522 0.005057 12.957 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lm <- lm(KUD95 ~ Spawn * Species, data = week_kuds)
boxplot(KUD95 ~ Spawn, data = week_kuds)
